% Template for ICIP-2013 paper; to be used with:
%          spconf.sty  - ICASSP/ICIP LaTeX style file, and
%          IEEEbib.bst - IEEE bibliography style file.
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\documentclass{article}
\usepackage{spconf,amsmath,graphicx}
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% Title.
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\title{Human head-shoulder detection using Hybrid Histogram of Oriented Optical Flow-HOG in low quality video }
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\name{
    Chun,F.Hsu,\quad
    Jayavardhana Gubbi,\quad
    Marimuthu Palaniswami
    \thanks{This work is partially supported by the ARC linkage project $\textrm{LP}100200430$, partnering the University of Melbourne, Melbourne Cricket Club and ARUP. Authors would like to thank ARUP  representatives (Paul Stanley, Andrew Maher, George Kazantzidis), MCG representative (Trevor Dohnt) and the staff of MCG.}}
%%
\address{
    ISSNIP, Department of Electrical and Electronic Engineering\\
    The University of Melbourne\\
    Parkville Campus, VIC - 3010, Australia\\
    \texttt{\small Email: fcchsu@student.unimelb.edu.au, \{jgl, palani\}@unimelb.edu.au}
    }

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\begin{document}
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\begin{abstract}
The detection of humans in surveillance video is still a challenging problem due to low quality of visual information, occlusion, and clutter environment. 
In this paper we present a new feature, the integral HOOF, (Histogram of Oriented Optical Flow) and design the system combining the HOG (Histogram of Oriented Gradient) on the problem of detecting human head and shoulder in a low quality surveillance video. We accelerate the integral HOOF process by using global cells map coding. The HOOF is not only merely a discriminative feature under low quality of video condition, but it also can segment moving objects from the image. After segmentation, the speed of extracting dense detection windows features of head and shoulder is improved. We extract HOOF and HOG from real world surveillance video, and use SVM (Support Vector Machine) to classify the heads and non-heads. Experimental shows the HOOF has high precision, recall and accuracy on detecting heads and ability to complements the HOG when encounters massive noises, blurred human figures, and low resolution pixels human figures in the video.
\end{abstract}
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\begin{keywords}
Histogram of Oriented Optical Flow, human detection
\end{keywords}
\input{introduction.tex}
\input{related_work.tex}
\input{sliding_window.tex}
\input{evaluation.tex}
\input{conclusion.tex}
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